In 2023, the greatest share of players by ethnic group in the National Football League (NFL) were black or African American athletes, constituting just over ** percent of players within the NFL. Despite the large population of Hispanic or Latino people within the United States, there is a substantial underrepresentation within the NFL, with only *** percent of players identifying as such. National Football League The National Football League (NFL) is a professional American football league that was established in 1920 and now consists of 32 clubs divided into two conferences, the National Football Conference (NFC) and the American Football Conference (AFC). The league culminates in the Super Bowl, the NFL's annual championship game. As the league’s championship game, the Super Bowl has grown into one of the world's largest single-day sporting events, attracting high television ratings and generating billions of dollars in consumer spending. NFL revenues The NFL is one of the most profitable sports leagues in the world, generating a staggering **** billion U.S. dollars in 2022. This total revenue of all ** NFL teams has constantly increased over the past 15 years and, although this figure dropped significantly in 2020, this was largely as a result of the impact of coronavirus (COVID-19) containment measures. This significant drop in revenue demonstrates one of the primary impacts of COVID-19 on professional sports leagues. NFL franchises As a result of this profitability in non-pandemic times, the franchises of the NFL are attributed extremely high market values. The Dallas Cowboys were by far the most valuable franchise in the NFL, with a market value of **** billion US dollars in 2023. The high value of NFL franchises can be seen clearly when compared to those of the NBA, MLB, and NHL. Franchises within the NFL had an average market value of approximately *** billion U.S. dollars in 2023.

Of the big four professional sports leagues in North America, the NFL and the NBA have the highest percentage of African American players. In 2024, around **** percent of NFL players were African American, as well as around **** percent of assistant coaches.

The National Football League comprises 32 teams from across the United States competing in two conferences split roughly by region. The NFL is one of the most popular professional sports leagues in the United States, with televised games attracting millions of viewers each week. This survey depicts the level of interest in the NFL in the United States, and it showed that 42 percent of Black respondents were avid fans of the NFL as of April 2023.

NFL Players Performance and Salary

Uncover Trends, Make Predictions and Analyze Demographics

By Ben Jones [source]

About this dataset

This Kaggle dataset contains unique and fascinating insights into the 2018-2019 season of the NFL. It provides comprehensive data such as player #, position, height, weight, age, experience level in years, college attended and the team they are playing for. All these attributes can be used to expand on research within the NFL community. From uncovering demographics of individual teams to discovering correlations between players' salaries and performance - this dataset has endless possibilities for researchers to dive deeply into. Whether you are searching for predictions about future seasons or creating complex analyses using this data - it will give you a detailed view of the 2018-2019 season like never before! Explore why each team is special, who shone individually that year and what strategies could have been employed more efficiently throughout with this captivating collection of 2019-2018 NFL Players Stats & Salaries!

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Featured Notebooks

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How to use the dataset

• Get familiar with the characteristics of each column in our data set: Rk, Player, Pos, Tm, Cap Hit Player # , HT , WT Age , Exp College Team Rk Tm . Understanding these columns is key for further analysis since you can use each attribute for unique insights about NFL players' salaries and performance during this season. For example, HT (height) and WT (weight) are useful information if you want to study any correlations between player body types and their salaries or game performances. Another example would be Pos (position); it is a critical factor that determines how much a team pays its players for specific roles on the field such as quarterbacks or running backs etc.
• Use some visualizations on your data as it helps us better understand what we observe from statistical data points when placed into graphical forms like scatter plots or bar charts. Graphical representations are fantastic at helping us see correlations in our datasets; they let us draw conclusions quickly by comparing datasets side by side or juxtaposing various attributes together in order explore varying trends across different teams of players etc.. Additionally, you could also represent all 32 teams graphically according to their Cap Hits so that viewers can spot any outlier values quickly without having to scan a table full of numbers – map based visualizations come extremely handy here!
• Employ analytical techniques such as regular expression matching (RegEx) if needed; RegEx enables us detect patterns within text fields within your datasets making them exceptionally useful when trying discovering insights from large strings like college team name URLSs [for example] . This could potentially lead you towards deeper exploration into why certain franchises may have higher salaried players than others etc..
• Finally don't forget all mathematical tools available at your disposal; statistics involves sophisticated operations like proportions / ratios/ averages/ medians - be sure take advantage these basic math features because quite often they end up revealing dazzling new facets inside your datasets which help uncover more interesting connections & relationships between two separate entities such as how does height compare against drafted college etc..?

We hope these tips help those looking forward unlocking hidden gems hidden

Research Ideas

• Analyzing the impact of position on salaries: This dataset can be used to compare salaries across different positions and analyze the correlations between players’ performance, experience, and salaries.
• Predicting future NFL MVP candidates: By analyzing popular statistical categories such as passing yards, touchdowns, interceptions and rushing yards for individual players over several seasons, researchers could use this data to predict future NFL MVPs each season.
• Exploring team demographics: By looking into individual teams' player statistics such as age, height and weight distribution, researchers can analyze and compare demographic trends across the league or within a single team during any given season

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even co...

As of August 2023, around half of NFL fans aged 18-29 were white. Meanwhile, ** percent of NFL fans aged 30 or older were Black, while this figure stood at ** percent among those aged 18 to 29.

Players rated by appearance for an analysis of the racial demographics of club rosters

The National Football League (NFL) is a professional football league in the United States comprising 32 teams spread out over two conferences. As one of the biggest professional sports leagues in the country, it is no surprise that the NFL attracts fans from all walks of life. In a 2020 survey, it was found that ** percent of Los Angeles fans were Hispanic, while ** percent were Black.

A January 2023 survey illustrated the average weekly viewership of National Football League games by respondents of different ethnicities in the United States. According to the survey, ** percent of Asian respondents stated that they spent between *** and **** hours a week following NFL games, whereas this figure amounted to ** percent for white survey participants. Meanwhile, ** percent of African American/Black and Latino respondents spent between *** and **** hours weekly watching football.

Fantasy Football Market Outlook

In 2023, the global fantasy football market size was valued at approximately USD 24.4 billion, and it is projected to reach USD 48.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.9%. This robust growth is driven by the increasing popularity of sports betting, the expansion of internet penetration, and the evolving digital landscape that has made fantasy sports more accessible to a global audience.

The burgeoning interest in fantasy football is significantly fueled by the thrill associated with virtual sports management and the competitive spirit it invokes among participants. The advent of high-speed internet and the proliferation of smartphones have considerably lowered entry barriers, enabling users from diverse demographics to engage with fantasy football platforms. Enhanced user interfaces and the strategic inclusion of real-time data and analytics have further enriched the user experience, making the game more immersive and engaging. Additionally, the growing partnerships between fantasy sports platforms and major sports leagues have enhanced the credibility and reach of the market.

Another crucial growth factor is the increasing monetization avenues within the fantasy football ecosystem. Platforms are leveraging ad revenues, subscription models, and in-app purchases to enhance their profitability. The introduction of innovative revenue streams like virtual goods, personalized content, and premium features provides substantial growth opportunities. Furthermore, the gamification of fantasy sports, including interactive features like social sharing and leaderboards, has significantly contributed to user retention and engagement.

Public perception and societal trends have also played a pivotal role in the market's growth. The cultural acceptance of fantasy sports as a mainstream activity has expanded its demographic reach beyond traditional sports enthusiasts. The integration of fantasy sports into mainstream media, including dedicated shows and podcasts, has increased visibility and user adoption. This cultural shift has also led to the formation of fantasy football communities, fostering a sense of camaraderie and collective enthusiasm.

American Football has played a pivotal role in the evolution of fantasy sports, particularly in North America, where the National Football League (NFL) stands as the most popular league for fantasy football. The deep-rooted passion for American Football among fans has translated into a robust fantasy football culture, with millions of participants engaging in both daily and season-long leagues. The NFL's extensive media coverage and the availability of detailed player statistics have made it an ideal sport for fantasy leagues, offering fans an opportunity to test their managerial skills and engage with the sport on a deeper level. This engagement is further amplified by the NFL's active promotion of fantasy football, which has helped to sustain and grow its fan base over the years.

From a regional perspective, North America continues to dominate the fantasy football market, driven by the high penetration of internet services and the strong sports culture in the region. The United States alone accounts for a significant portion of the market owing to the popularity of the National Football League (NFL). Meanwhile, Europe and the Asia Pacific are emerging as significant growth regions. The increasing popularity of soccer and the rising number of internet users in countries like India and China are expected to contribute to the market's expansion in these regions.

Platform Analysis

The fantasy football market is segmented into mobile applications and websites based on the platform. Mobile applications have revolutionized the fantasy sports experience by offering users the convenience of managing their teams on the go. The advent of sophisticated mobile apps with user-friendly interfaces, real-time updates, and interactive features has significantly enhanced user engagement. The integration of advanced analytics and personalized recommendations in mobile applications has made it easier for users to make informed decisions, thereby increasing user satisfaction and retention.

On the other hand, websites continue to be a popular platform among a segment of users who prefer a more detailed and expansive interface. Websites offer a broader range of features and functionalities compared to mobile applicatio

Content

NFL Fandom

This folder contains data behind the story How Every NFL Team’s Fans Lean Politically.

Google Trends Data

Google Trends data was derived from comparing 5-year search traffic for the 7 sports leagues we analyzed:

https://g.co/trends/5P8aa

Results are listed by designated market area (DMA).

The percentages are the approximate percentage of major-sports searches that were conducted for each league.

Trump's percentage is his share of the vote within the DMA in the 2016 presidential election.

SurveyMonkey Data

SurveyMonkey data was derived from a poll of American adults ages 18 and older, conducted between Sept. 1-7, 2017.

Listed numbers are the raw totals for respondents who ranked a given NFL team among their three favorites, and how many identified with a given party (further broken down by race). We also list the percentages of the entire sample that identified with each party, and were of each race.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

American Football is one of the most popular sports in the United States, enjoyed by thousands of people live in the stadiums and millions more back home. The highest league in the land is the NFL, which consists of 32 teams divided equally into two conferences. At the end of the season, the two best-performing teams compete for the ultimate prize at the Super Bowl. College football is seen as the final step up towards the NFL, with the best performers on the college circuit gaining lucrative contracts with NFL teams at the end of each season during the NFL Draft. A January 2020 survey showed that 12 percent of African American respondents very much preferred watching college football to NFL on TV.

The National Football League (NFL) is a professional football league in the United States. The league was founded in 1920 as the American Professional Football Association and played its inaugural season with ****** teams. Nowadays, the NFL comprises ** teams spread out over two conferences, the National Football Conference (NFC) and the American Football Conference (AFC), that each consist of **** divisions. In a September 2020 survey in the United States, ** percent of Hispanic respondents stated that they attended about *** game per season on average.

The "Big5 Soccer Leagues Players statistics Last5 Years" dataset offers a comprehensive collection of football player statistics spanning the last five years across the top five football leagues globally. With a rich repository of player data meticulously curated from prominent leagues including the Premier League, La Liga, Serie A, Bundesliga, and Ligue 1, this dataset serves as a valuable resource for in-depth analysis, research, and insights into the performance and trends of football players in elite competitions.

Encompassing a diverse array of player attributes, performance metrics, and match statistics, this dataset enables researchers, analysts, and football enthusiasts to delve deep into various aspects of player performance and team dynamics. From basic player information such as age, nationality, and position, to advanced metrics including goals scored, assists provided, successful passes, interceptions, and defensive contributions, this dataset offers a comprehensive overview of player performance across multiple seasons and competitions.

Key features of the dataset include:

Comprehensive Coverage: The dataset spans across multiple seasons, providing a comprehensive view of player performance trends over time. Multi-League Data: Data is sourced from the top five football leagues globally, offering insights into the performances of players across different footballing cultures and playing styles. Rich Player Attributes: In addition to basic demographic information, the dataset includes a wide range of player attributes and performance metrics, allowing for detailed analysis of player capabilities and contributions on the field. Versatile Analysis Opportunities: Whether it's evaluating player consistency, assessing team dynamics, identifying emerging talents, or uncovering tactical trends, this dataset presents versatile analysis opportunities for researchers and analysts alike. Utilizing this dataset, analysts can explore a myriad of research questions and hypotheses, ranging from individual player performance evaluations to broader team strategies and league-wide trends. From scouting potential transfer targets to optimizing team formations and tactics, the insights derived from this dataset can inform decision-making processes across various facets of football management and analysis.

In summary, the "Big5 Soccer Leagues Players statistics Last5 Years" dataset serves as a comprehensive repository of football player statistics, offering valuable insights and analysis opportunities for researchers, analysts, coaches, scouts, and football enthusiasts passionate about unraveling the intricacies of the beautiful game.

Source: This dataset contains player profile information originally sourced from Wyscout.

Content: The dataset includes biographical and physical details for football players. Key information includes: * Unique Wyscout player identifier (wyId) * Player names (firstName, lastName, shortName) * Demographics: birthDate, passportArea (including name, ID, alpha3code), birthArea * Physical attributes: height (cm), weight (kg), preferred foot * Team information: currentTeamId, currentNationalTeamId * Playing role: role (including code2, code3, and full name like 'Goalkeeper')

Structure: The data is provided in JSON. Note that some fields (passportArea, birthArea, role) contain nested JSON-like structures providing more detailed information.

Potential Use Cases: * Demographic analysis of player populations * Exploratory data analysis on player attributes (height, weight, age) by position * Foundation for merging with other football datasets (e.g., match events, market values) * Educational purposes for data analysis and visualization

Brief demographic information for the enrolled subjects.

Comprehensive dataset of college football teams ranked by first round nfl draft picks. Includes historical data, statistics, and performance metrics for NCAA Division I FBS teams.

This dataset provides detailed information on the top 500 football players in 2024, including their market values, performance statistics, and demographics. Key features include:

1. Market values ranging from €200M (Haaland, Vinicius Jr.) to €20M
2. Player statistics including goals, assists, and appearances
3. Demographic data including age (17-37) and nationality
4. Club affiliations across major leagues
5. Position-specific information
6. Performance metrics including yellow/red cards and substitution patterns

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease associated with head trauma. Although initially believed to affect only boxers, the at-risk population has expanded to encompass a much wider demographic, including American football players, hockey players, wrestlers, and military veterans. This expansion has garnered considerable media attention and public concern for the potential neurodegenerative effects of head trauma. The main aim of this systematic review is to give a complete overview of the common findings and risk factors for CTE as well as the status quo regarding the incidence and prevalence of CTE. This systematic review was performed using PubMed and MEDLINE and includes all neuropathologically confirmed cases of CTE in the medical literature to date, from the first published case in 1954 to August 1, 2013 (n = 153). The demographics, including the primary source of mTBI (mild Traumatic Brain Injury), age and cause of death, ApoE genotype, and history of substance abuse, when listed, were obtained from each case report. The demographics of American football players found to have CTE are also presented separately in order to highlight the most prevalent group of CTE cases reported in recent years. These 153 case reports of CTE represent the largest collection to date. We found that a history of mTBI was the only risk factor consistently associated with CTE. In addition, we found no relationships between CTE and age of death or abnormal ApoE allele. Suicide and the presence of premorbid dementia was not strongly associated with CTE. We conclude that the incidence of CTE remains unknown due to the lack of large, longitudinal studies. Furthermore, the neuropathological and clinical findings related to CTE overlap with many common neurodegenerative diseases. Our review reveals significant limitations of the current CTE case reporting and questions the widespread existence of CTE in contact sports.

Professional sports leagues in North America have traditionally been home to athletes from many backgrounds and ethnicities. In the 2023 season, over ** percent of the players in the National Basketball Association (NBA) were African American. In comparison, **** percent of NBA athletes were white and *** percent were Hispanic.

Demographic characteristics of parents, coaches, and organizational leaders.